Rapid odor recognition based on reliefF algorithm using electronic nose and its application in fruit identification and classification

Wen, Juhong; Zhao, Yongli; Qiu, Rong; Yang, Zhimeng; Yin, Jianxin; Peng, Zhi

doi:10.1007/s11694-022-01351-z

Cited by 18 publications

(9 citation statements)

References 32 publications

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“…To overcome this shortcoming, this paper proposes a feature extraction method through fitting the start stage of the gas response curve with polynomials. In order to compare the effectiveness of the proposed feature extraction method, we also constructed the manual feature dataset commonly used for E-nose based on previous studies [ 10 , 22 ]. The details of the construction process of each feature dataset are as follows:…”

Section: Resultsmentioning

confidence: 99%

“…Electronic nose (E-nose) is a system designed based on this principle to mimic the biological olfactory function. It has been applied in many fields, such as agriculture and the food industry [ 10 ], the environment [ 11 ], and disease diagnosis [ 9 ]. When CH 4 , CO, or their mixed gases exist in the ambient air, it is expected to realize the detection and identification of such gases by using E-nose.…”

Section: Introductionmentioning

confidence: 99%

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Rapid Identification Method for CH4/CO/CH4-CO Gas Mixtures Based on Electronic Nose

Yin

Zhao

Peng

et al. 2023

Sensors

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The inherent cross-sensitivity of semiconductor gas sensors makes them extremely challenging to accurately detect mixed gases. In order to solve this problem, this paper designed an electronic nose (E-nose) with seven gas sensors and proposed a rapid method for identifying CH4, CO, and their mixtures. Most reported methods for E-nose were based on analyzing the entire response process and employing complex algorithms, such as neural network, which result in long time-consuming processes for gas detection and identification. To overcome these shortcomings, this paper firstly proposes a way to shorten the gas detection time by analyzing only the start stage of the E-nose response instead of the entire response process. Subsequently, two polynomial fitting methods for extracting gas features are designed according to the characteristics of the E-nose response curves. Finally, in order to shorten the time consumption of calculation and reduce the complexity of the identification model, linear discriminant analysis (LDA) is introduced to reduce the dimensionality of the extracted feature datasets, and an XGBoost-based gas identification model is trained using the LDA optimized feature datasets. The experimental results show that the proposed method can shorten the gas detection time, obtain sufficient gas features, and achieve nearly 100% identification accuracy for CH4, CO, and their mixed gases.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Rapid Identification Method for CH4/CO/CH4-CO Gas Mixtures Based on Electronic Nose

Yin

Zhao

Peng

et al. 2023

Sensors

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“…For example, Liu et al [17] combined gas chromatography-mass spectrometry with an E-nose to analyze the grinding flavor of pepper and found that the aroma concentration of pepper ground at a low temperature was higher than the traditional milling method. Wen et al [18] achieved the accurate identification of fruit freshness based on fruity aromas by using E-nose. Heidarbeigi et al [19] found that the E-nose technique can identify saffron with an adulteration rate higher than 10%.…”

Section: Introductionmentioning

confidence: 99%

“…Extracting effective odor features from the response signals and then training models are the two key steps for odor identification by using the E-nose. At present, the commonly used ways to extract the odor features are time-domain features [18,20,21], frequency domain features [22,23], curve fitting [24][25][26], and data compression [27,28]. However, the effectiveness of the feature-extraction methods is affected by many factors, such as the target gases and the application scenarios.…”

Section: Introductionmentioning

confidence: 99%

Identification of Adulterants in Chili Powder Based on the Histogram of Oriented Gradients Algorithm by Using an Electronic Nose

Peng,

Ba,

Zhang

et al. 2024

Applied Sciences

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Driven by economic interests, adding adulterations in chili powder is a problem which threatens people’s health. Thanks to its nondestructive, rapid, and portable advantages, electronic nose has more potential to be used for adulteration detection than the traditional methods. An approach for identifying the adulterants in chili powder was proposed in this paper. Firstly, an electronic nose system with 10 gas sensors was designed, and then the response images were drawn based on the response signals of the electronic nose. Afterwards, gas features were extracted from those response images by using a histogram of oriented gradients (HOG) algorithm. Finally, an SVM-based identification model was constructed to achieve the identification of plant adulterants in chili powder. The experimental results showed that the identification accuracy of the adulterant categories (almond shell, red beetroot, and tomato peel) based on the HOG features could reach up to 98.3%, and the identification results for adulterant content were 94.2%, 93.3%, and 95%, respectively. Furthermore, in order to compare the efficiency of the proposed identification approach, the widely used model AlexNet was also investigated and discussed.

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“…30,31 Several methods have been proposed for the feature selection of a sensor array system, which can be mainly divided into three categories: filter, wrapper, and embedded. 32 Filter methods select important features based on predefined criteria, such as correlation criteria, 33 mutual information, 34,35 reliefF method, 36,37 and Wilks' Λ-statistic. [38][39][40] Wrapper methods, such as genetic algorithm, 41,42 particle swarm optimization, 19,42 and recursive backward selection method, 43 support vector machine-backward feature elimination with cross-validation 44 and random forest recursive feature elimination, 45 search for the optimal feature subset with the best performance of specific pattern recognition algorithm.…”

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confidence: 99%

Multi-feature Optimization of a Gas Sensor Array to Improve Component and Concentration Detection Performance of VOC Mixtures

Zhao²,

Wang

et al. 2023

J. Electrochem. Soc.

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Many studies focus on feature extraction and selection of gas sensor arrays for gas identification. In this work, we intended to find a feature subset obtained by selecting the most important features for simultaneously improving component and concentration detection performance of a gas sensor array to three harmful volatile organic compounds (VOCs, toluene, methanol, and ethanol) and their mixtures. First, 30 features were extracted from six sensors’ responses to form a multi-feature set. Then, two feature selection methods based on Wilks’ Λ-statistic and random forest were employed to obtain the best feature combination. Seven out of 30 features were finally selected to form the optimal feature set. The gas identification accuracy is 94.3%, and the concentration estimation error is 0.79 ppm (RMSE). Through feature selection, not only qualitative and quantitative analyses performance of VOCs mixtures are significantly improved, but also system complexity (6 to 4 sensors) and computation cost (by about 15%) are effectively reduced.

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Rapid odor recognition based on reliefF algorithm using electronic nose and its application in fruit identification and classification

Cited by 18 publications

References 32 publications

Rapid Identification Method for CH4/CO/CH4-CO Gas Mixtures Based on Electronic Nose

Rapid Identification Method for CH4/CO/CH4-CO Gas Mixtures Based on Electronic Nose

Identification of Adulterants in Chili Powder Based on the Histogram of Oriented Gradients Algorithm by Using an Electronic Nose

Multi-feature Optimization of a Gas Sensor Array to Improve Component and Concentration Detection Performance of VOC Mixtures

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